As an emerging dynamic modeling method that incorporates time-dependent heterogeneity, hidden Markov models (HMM) are receiving increased research attention with regards to travel behavior modeling and travel demand forecasting. This paper focuses on the model transferability of HMM. Based on a series of transferability and goodness-of-fit measures, it finds that HMMs have a superior performance in predicting future transportation mode choice, compared to conventional choice models. Aimed at further enhancing its transferability, this paper proposes a Bayesian conditional recalibration approach that maps the model prediction directly to the context data. Compared to traditional model transferring methods, the proposed approach does not assume fixed parameterization and recalibrates the utilities and the prediction directly. A comparison between the proposed approach and the traditional transfer-scaling favors our approach, with higher goodness-of-fit. This paper fills the gap in understanding the transferability of HMM and proposes a practical method that enables potential applications of HMM.
针对某型舰用蒸汽动力装置凝给水系统的性能可靠性受多指标综合约束的问题,采用一种基于物理模型与Mont-Carlo联合仿真的方法对该系统的性能可靠性进行分析.提出"冷凝器水位超出高限,或除氧器、锅炉水位小于低限"三重约束构成的系统故障判据;在凝水-增压泵级间密封结构退化模型的基础上,结合系统热力学模型得到凝给水系统性能退化模型;以Degraded with system running time、用以衡量级间密封结构节流能力的导纳系数为输入,以获得锅炉升负荷过程中冷凝器、除氧器和锅炉的水位极值并将其与故障判据相对比为策略,对级间密封结构退化影响下的凝给水系统的性能可靠性进行仿真分析,得到系统性能失效特征量随时间的退化规律. 相似文献